Introduction

Load libraries

Define colors

Load single cell RNA-Seq data

Figure 4a

Figure 4b

Figure 4c

Figure 4d

Figure 4e

Redefine scRepertoire function

To be able to change few ploting parameters of the clonalHomeostasis function, we copy/paste and modified some function from scRepertoire

Open clonotype data

n= 29690 TCR with EXACTLY one alpha chain CDR3 and one beta chain CDR3

Add transcriptomic data

if(params$accessibility == "unlock"){

  srat <- RenameCells(srat,new.names = paste0(srat$patient, "_",gsub("(_1|_2|_3|_4|_5|_6|_7|_8|_9)*", "",colnames(srat))))
  srat <- RenameCells(srat,new.names = gsub("_post", "",colnames(srat)))

  srat$sample <- gsub("_post", "",srat$patient)

  seurat <- combineExpression(combined, srat, 
                  cloneTypes = c(Rare = .00005, Small = .0005, 
                        Medium = .005, Large = .05, Hyperexpanded = 1),
                        cloneCall = "strict", 
                  group.by = "sample", chain = "both",
                  proportion = TRUE,
                  filterNA = TRUE)



  s    <- subset(seurat, subset = anno_l1 %in% c("CD8+ T cells","CD4+ T cells", "Proliferating cells", "Tregs", "Natural killer cells"))

  s$cloneType <- droplevels(s$cloneType)

  s@meta.data$Pathological.Response <- factor(s@meta.data$Pathological.Response, levels = c("pCR", "non-pCR"))



} else{
  print("Please request access to the BCR-Seq data.")
}

We mapped 26528 TCR to the scRNA-Seq data, including 23099 to annotated T cells (10699 CD4 T cells, 1951 Tregs and 9517 CD8 T cells). Only 3 patients do not have hyperexpanded clones (2 non-pCR and 1 pCR).

if(params$accessibility == "unlock"){

  ss <- subset(seurat, subset = anno_l1 %in% c("CD8+ T cells") )
  ss@meta.data$cloneType <- factor(ss@meta.data$cloneType, levels = c("Hyperexpanded (0.05 < X <= 1)", "Large (0.005 < X <= 0.05)", "Medium (5e-04 < X <= 0.005)", "Small (5e-05 < X <= 5e-04)"))
  # define cell group membership
  Idents(ss) <- ss$cloneType

  de_markers <- DElegate::FindAllMarkers2(ss, replicate_column = "patient", method = "edger", min_fc = log2(1), min_rate = 0.1)



  signature_h <- de_markers$feature[de_markers$group1=="Hyperexpanded (0.05 < X <= 1)"]
  background <- rownames(ss)

  # enricher for cnet plot
  GO_H_gene_sets = msigdbr(species = "human", category = "H")
  msigdbr_t2g = GO_H_gene_sets %>% dplyr::distinct(gs_name, gene_symbol) %>% as.data.frame()

  ego_h <- enricher(gene = signature_h, universe = background, TERM2GENE = msigdbr_t2g)

 
  b <- barplot(ego_h, title = "Hallmarks´enrichment of hyperexpanded clones")


  tmp <- b$data
  
  tmp$ID <- str_trunc(tmp$ID, 100, "right")
  tmp$ID <- factor(tmp$ID, levels = tmp$ID[order(tmp$Count)])

  p <- ggplot(tmp, aes(Count, ID)) +
    geom_bar(stat = "identity", color="black", fill =  colors_clonotype["Hyperexpanded (0.05 < X <= 1)"]) +
   theme_classic()+ 
    geom_text(
        aes(label = (paste( "padj=", formatC(p.adjust, format = "e", digits = 2)))),
        color = "black",
        size = 6,
        hjust=1,
        position = position_dodge(0.5)  ) +
     theme(text = element_text(size=24)) +
  ggtitle(" Hyperexpanded \n CD8+ T cells clones")

  print(p)


DT::datatable(p$data, 
              caption = ("Figure 4e"),
              extensions = 'Buttons', 
              options = list( dom = 'Bfrtip',
              buttons = c( 'csv', 'excel')))
} else{
  print("Please request access to the BCR-Seq data.")
}

Figure 4f

We maped 1286 TRA out of 23099 detected TRA (7.3%) We maped 435 unique TRA out of 10741 unique TRA chains (4%).

if(params$accessibility == "unlock"){

  McPAS <- utils::read.csv(params$McPAS)
  head(McPAS)

  a <- colsplit(s$CTaa, "_", name=c("CTaa_TRA", "CTaa_TRB"))
  rownames(a) <- colnames(s)
  a$rid <- rownames(a)


  c <- McPAS[McPAS$CDR3.alpha.aa %in%  na.omit(a$CTaa_TRA), c("CDR3.alpha.aa", "Category", "Pathology")]
  head(c)
  colnames(c) <- c("CTaa_TRA",  "Category", "Pathology")



  joint <- left_join(a, c, by = "CTaa_TRA")
  joint <- joint[joint$rid %in% colnames(s),]
  joint <- joint[!duplicated(joint$rid),]
  rownames(joint) <- (joint$rid)
  
  s <- AddMetaData(s, joint)

  Idents(s) <- "Pathology"
  cells <- WhichCells(s, ident =  c("Human herpes virus 1" ) )

  Idents(s) <- "Category"
  s <- SetIdent(s, cells = cells, value = paste0("hHSV: ", s@meta.data$CTaa[s@meta.data$Pathology %in% c("Human herpes virus 1")]))
  s$Category <- Idents(s)

  Idents(s) <- "Pathology"
  cells <- WhichCells(s, ident =  c("Herpes simplex virus 2 (HSV2)") )
  Idents(s) <- "Category"
  s <- SetIdent(s, cells = cells,  value = paste0("hHSV: ", s@meta.data$CTaa[s@meta.data$Pathology %in% c("Herpes simplex virus 2 (HSV2)")]))
  s$Category <- Idents(s)

} else{
  print("Please request access to the BCR-Seq data.")
}
if(params$accessibility == "unlock"){

  Idents(s) <- "Category"
  cells <- WhichCells(s, ident =  c("Pathogens","Autoimmune", "Allergy" ) )
  Idents(s) <- "Category"
  s <- SetIdent(s, cells = cells, value = "Other")
  s$Category <- Idents(s)
  s <- subset(s, subset = Category != "Other")
  s$Category <- droplevels(s$Category)

  p3 <- SCpubr::do_BarPlot(subset(s, subset = Category != "NA" & Category != "Other"  & Pathological.Response == "pCR"),
                         group.by = "Category",
                         split.by = "patient",
                         order=FALSE,
                         legend.position = "right",
                        colors.use = colors_McPAS,
                        font.size = 28, flip = TRUE) + xlab("pCR")

  p4 <- SCpubr::do_BarPlot(subset(s, subset = Category != "NA" & Category != "Other"& Pathological.Response == "non-pCR"),
                         group.by = "Category",
                         split.by = "patient",
                         order=FALSE,
                         legend.position = "right",
                         colors.use = colors_McPAS,
                   font.size = 28, flip = TRUE) + xlab("non-pCR")

  p3$labels$y <- " "
  
  p <- p3 + p4 + plot_layout(ncol = 1,  heights   = c(1,2),  guides = "collect") + ylab("Number of mapped TCR")
  print(p)

 
  DT::datatable(rbind(p3$data, p4$data), 
              caption = ("Figure 4f"),
              extensions = 'Buttons', 
              options = list( dom = 'Bfrtip',
              buttons = c( 'csv', 'excel')))

} else{
  print("Please request access to the BCR-Seq data.")
}

Session Info

## R version 4.3.0 (2023-04-21)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Europe/Vienna
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] stringr_1.5.1         msigdbr_7.5.1         DOSE_3.26.2           org.Hs.eg.db_3.17.0  
##  [5] AnnotationDbi_1.62.2  IRanges_2.34.1        S4Vectors_0.38.2      Biobase_2.60.0       
##  [9] BiocGenerics_0.46.0   clusterProfiler_4.8.3 enrichplot_1.20.3     scales_1.3.0         
## [13] RColorBrewer_1.1-3    ggnewscale_0.4.10     tidyr_1.3.1           scRepertoire_1.10.1  
## [17] dittoSeq_1.12.2       canceRbits_0.1.6      ggpubr_0.6.0.999      ggplot2_3.5.1        
## [21] viridis_0.6.5         viridisLite_0.4.2     reshape2_1.4.4        tibble_3.2.1         
## [25] SCpubr_2.0.2          DT_0.32               patchwork_1.2.0       dplyr_1.1.4          
## [29] Seurat_5.0.3          SeuratObject_5.0.1    sp_2.1-3             
## 
## loaded via a namespace (and not attached):
##   [1] fs_1.6.4                    matrixStats_1.2.0           spatstat.sparse_3.0-3      
##   [4] bitops_1.0-7                HDO.db_0.99.1               httr_1.4.7                 
##   [7] doParallel_1.0.17           tools_4.3.0                 sctransform_0.4.1          
##  [10] backports_1.4.1             utf8_1.2.4                  R6_2.5.1                   
##  [13] vegan_2.6-4                 lazyeval_0.2.2              uwot_0.1.16                
##  [16] mgcv_1.9-1                  permute_0.9-7               withr_3.0.0                
##  [19] gridExtra_2.3               progressr_0.14.0            cli_3.6.2                  
##  [22] spatstat.explore_3.2-7      fastDummies_1.7.3           scatterpie_0.2.1           
##  [25] isoband_0.2.7               labeling_0.4.3              sass_0.4.9                 
##  [28] spatstat.data_3.0-4         ggridges_0.5.6              pbapply_1.7-2              
##  [31] yulab.utils_0.1.4           gson_0.1.0                  stringdist_0.9.12          
##  [34] parallelly_1.37.1           limma_3.56.2                RSQLite_2.3.5              
##  [37] VGAM_1.1-10                 rstudioapi_0.16.0           generics_0.1.3             
##  [40] gridGraphics_0.5-1          ica_1.0-3                   spatstat.random_3.2-3      
##  [43] crosstalk_1.2.1             car_3.1-2                   GO.db_3.17.0               
##  [46] Matrix_1.6-5                ggbeeswarm_0.7.2            fansi_1.0.6                
##  [49] abind_1.4-5                 lifecycle_1.0.4             edgeR_3.42.4               
##  [52] yaml_2.3.8                  carData_3.0-5               SummarizedExperiment_1.30.2
##  [55] qvalue_2.32.0               Rtsne_0.17                  blob_1.2.4                 
##  [58] grid_4.3.0                  promises_1.2.1              crayon_1.5.2               
##  [61] miniUI_0.1.1.1              lattice_0.22-6              cowplot_1.1.3              
##  [64] KEGGREST_1.40.1             pillar_1.9.0                knitr_1.45                 
##  [67] fgsea_1.26.0                GenomicRanges_1.52.1        future.apply_1.11.1        
##  [70] codetools_0.2-19            fastmatch_1.1-4             leiden_0.4.3.1             
##  [73] glue_1.7.0                  downloader_0.4              ggfun_0.1.5                
##  [76] data.table_1.15.2           treeio_1.24.3               vctrs_0.6.5                
##  [79] png_0.1-8                   spam_2.10-0                 gtable_0.3.5               
##  [82] assertthat_0.2.1            cachem_1.1.0                xfun_0.43                  
##  [85] S4Arrays_1.0.6              mime_0.12                   tidygraph_1.3.1            
##  [88] survival_3.5-8              DElegate_1.2.1              SingleCellExperiment_1.22.0
##  [91] pheatmap_1.0.12             iterators_1.0.14            fitdistrplus_1.1-11        
##  [94] ROCR_1.0-11                 nlme_3.1-164                ggtree_3.13.0.001          
##  [97] bit64_4.0.5                 RcppAnnoy_0.0.22            evd_2.3-6.1                
## [100] GenomeInfoDb_1.36.4         bslib_0.6.2                 irlba_2.3.5.1              
## [103] vipor_0.4.7                 KernSmooth_2.23-22          DBI_1.2.2                  
## [106] colorspace_2.1-0            ggrastr_1.0.2               tidyselect_1.2.1           
## [109] bit_4.0.5                   compiler_4.3.0              SparseM_1.81               
## [112] DelayedArray_0.26.7         plotly_4.10.4               shadowtext_0.1.3           
## [115] lmtest_0.9-40               digest_0.6.35               goftest_1.2-3              
## [118] spatstat.utils_3.0-4        rmarkdown_2.26              XVector_0.40.0             
## [121] htmltools_0.5.8             pkgconfig_2.0.3             sparseMatrixStats_1.12.2   
## [124] MatrixGenerics_1.12.3       highr_0.10                  fastmap_1.2.0              
## [127] rlang_1.1.4                 htmlwidgets_1.6.4           shiny_1.8.1                
## [130] farver_2.1.2                jquerylib_0.1.4             zoo_1.8-12                 
## [133] jsonlite_1.8.8              BiocParallel_1.34.2         GOSemSim_2.26.1            
## [136] RCurl_1.98-1.14             magrittr_2.0.3              GenomeInfoDbData_1.2.10    
## [139] ggplotify_0.1.2             dotCall64_1.1-1             munsell_0.5.1              
## [142] Rcpp_1.0.12                 evmix_2.12                  babelgene_22.9             
## [145] ape_5.8                     reticulate_1.35.0           truncdist_1.0-2            
## [148] stringi_1.8.4               ggalluvial_0.12.5           ggraph_2.2.1               
## [151] zlibbioc_1.46.0             MASS_7.3-60.0.1             plyr_1.8.9                 
## [154] parallel_4.3.0              listenv_0.9.1               ggrepel_0.9.5              
## [157] forcats_1.0.0               deldir_2.0-4                Biostrings_2.68.1          
## [160] graphlayouts_1.1.1          splines_4.3.0               tensor_1.5                 
## [163] locfit_1.5-9.9              igraph_2.0.3                spatstat.geom_3.2-9        
## [166] cubature_2.1.0              ggsignif_0.6.4              RcppHNSW_0.6.0             
## [169] evaluate_0.23               foreach_1.5.2               tweenr_2.0.3               
## [172] httpuv_1.6.15               RANN_2.6.1                  purrr_1.0.2                
## [175] polyclip_1.10-6             future_1.33.2               scattermore_1.2            
## [178] ggforce_0.4.2               broom_1.0.5                 xtable_1.8-4               
## [181] tidytree_0.4.6              RSpectra_0.16-1             rstatix_0.7.2              
## [184] later_1.3.2                 gsl_2.1-8                   aplot_0.2.3                
## [187] beeswarm_0.4.0              memoise_2.0.1               cluster_2.1.6              
## [190] powerTCR_1.20.0             globals_0.16.3